The study of distributed multi-robot systems (MRS) has received increased attention in recent years. This is not surprising as continually improving technology has made the deployment of MRS consisting of increasingly larger numbers of robots possible. With the growing interest in MRS comes the expectation that, at least in some important respects, multiple robots will be superior to a single robot in achieving a given task.

Potential advantages of MRS over a single robot system (SRS) are frequently discussed in the literature. For example, total system cost, it is frequently claimed, may be reduced by utilizing multiple simple and cheap robots as opposed to a single complex and expensive robot. Furthermore, the inherent complexity of some task environments may require the use of multiple robots as the necessary capabilities are too substantial to be met by a single robot. Finally, multiple robots are often assumed to increase system robustness by taking advantage of inherent parallelism and redundancy. Therefore, negative effects on task performance caused by individual robot failure or the dynamic addition or removal of individual robots can be minimized.

However, the utilization of MRS poses potential disadvantages and additional challenges that must be addressed if MRS are to present a viable and effective alternative to SRS. A poorly designed MRS, with individual robots working toward opposing goals, can be less effective than a carefully designed SRS. As such, in most cases just taking a suitable SRS and scaling it up to multiple robots is not adequate. A paramount challenge in the design of effective MRS is managing the complexity introduced by multiple, interacting robots. In order to maximize the effectiveness of a task-achieving MRS, the robots' actions must be spatiotemporally coordinated and directed towards the achievement of a given system-level task.

Unfortunately, the design of coordinated distributed MRS remains more of an art than a science. Current design methodologies are, in the best case, driven by informal and undocumented expert knowledge. In the worst case, MRS design methodologies are driven by resource-intensive trial-and-error processes. Demonstrated systems are usually highly task-specific and are infrequently accompanied by formal analysis of the expected system performance or bounds of applicability. Furthermore, formal explanations are rarely provided to justify the superiority of the given system relative to possible alternative designs.

This research project aims to align the design process closer to the realm of science through the advent of a principled distributed MRS reactive controller design methodology. The presented design methodology is composed of integrated robot controller synthesis methods and methods for the predictive analysis of resulting system behavior.

Our principled MRS controller design methodology provides a framework for precisely defining and reasoning about the intertwined entities intrinsically involved in any coordinated MRS -- the world, task definition, and the capabilities of the robots themselves, including action selection, sensing, maintenance of internal state, and inter-robot communication. This framework provides a formal foundation that is used in an integrated suite of robot controller synthesis methods and MRS modeling methods used to produce effective controllers for task-directed MRS. Figure 1 provides a high-level diagram of the design methodology and additional details on the MRS synthesis and analysis methods are provided below.

Figure 1: High-level diagram of the MRS design methodology.

Synthesis

Synthesis is the process of constructing a specific instance of a MRS which meets design requirements such as achieving the desired level of task performance while meeting constraints imposed by limited robot capabilities. Our approach to the synthesis of coordinated MRS is to use the formal framework mentioned above in a prescriptive fashion. This formalism provides a foundation upon which our synthesis approaches are derived. Specifically, we synthesize a MRS by synthesizing individual robot controllers, such that when every robot in the MRS executes the controller, system-level task-directed coordination is achieved. Typically, robot controllers are synthesized by hand, relying on expert knowledge of the designer and an intimate understanding of how the robots will interact with each other and with the world during task execution. Our approach is novel in that it represents a systematic and automated method for MRS synthesis. The designer's responsibility is reduced to defining the task domain using the formal framework, information a hand-designed system inherently requires as well. Using this formal definition of the task domain, the synthesis methods automatically produce a complete robot controller that may be executed by all robots in the MRS to achieve task-directed system-level coordination.

The synthesis methods we have developed represent a limited but important sampling of the space of possibilities and provide a means to study the formal synthesis of a range of MRS using a variety of common robot coordination and control features, such as probabilistic action selection, the maintenance of persistent internal state [4,5,6], and the use of inter-robot communication [2].

Analysis

The design of an effective task-directed MRS is often difficult because there is not an accurate understanding of the relationship between different design options and resulting task performance. In the common trial-and-error design process, the designer will construct a MRS and then try it out in either a simulation or on actual robots. Either way, this process is resource-intensive as it requires much effort and time to evaluate many possible designs. Ideally, the designer should be equipped with an analytical tool for the analysis of a potential MRS design. Such a tool would allow for quick and efficient evaluation of different design options and likely result in more effective MRS designs as the design can more effectively be optimized with respect to some desired performance metric.

Our formal MRS design methodology includes the capability for analytical evaluation of potential MRS designs. We have developed two MRS models based on the MRS formalism that we use for the analysis of MRS. The first is a Bayesian macroscopic MRS modeling approach [5] and the second is a probabilistic microscopic MRS modeling approach.

Using both physically-realistic simulation and physical robots, we have experimentally demonstrated our formal approach to the synthesis of coordinated MRS in a multi-robot construction task. This task requires the sequential placement of a series of cubic colored bricks into a planar structure. The construction task starts with a seed structure, which is a small number of initially placed bricks forming the core of the structure.

Our simulation experiments were performed using Player and the Gazebo simulation environment. Player [10] is a server that connects robots, sensors, and control programs over a network. Gazebo [11] simulates a set of Player devices in a 3-D physically-realistic world with full dynamics. Together, the two represent a high-fidelity simulation tool for individual robots and teams that has been validated on a collection of real-robot robot experiments using Player control programs transferred directly to physical Pioneer 2DX mobile robots. In all simulation experiments 8 robots were used, and in all real-robot experiments 3 robots were used. The robots were either realistic models of or actual ActivMedia Pioneer 2DX mobile robots. Each robot, approximately 30 cm in diameter, is equipped with a differential drive, a forward-facing 180 degree scanning laser rangefinder, and a forward-looking color camera with a 100-degree field-of-view and a color blob detection system. The bricks are taller than the robot's sensors, so the robots can only sense the local bricks on the periphery of the structure (i.e., robots do not have a birds-eye view of the entire structure).

Details on the application of our MRS design methodology can be found in [2,4,5,6,7]. Futhermore, videos of the resulting MRS design can be seen demonstrated in simulation and on physical robots in the videos below.